The present invention relates to a plant control apparatus for controlling a plant such as a thermal power plant.
A plant control apparatus processes signals measured at a plant to be controlled to calculate control signals to be applied to the control objects. The control apparatus is equipped with an algorithm for calculating control signals so as to make signals measured at the plant satisfy target values.
There is a proportion/integration (PI) control algorithm which is used as a plant control algorithm. With PI control, a deviation between a plant measurement signal and a target value is multiplied by a proportional gain, and this multiplied value is added to a time integration value of the deviation to obtain a control signal to be applied to the control targets. The PI control algorithm can be visualized by using a block diagram or the like, and the operation principle thereof can be understood easily. The block diagram of this type is generally created by a control logic designer, and the control algorithm is free from the operation not intended by the designer. The PI control algorithm is stable and safe, and has been used in various fields.
However, if a plant is run under the conditions not anticipated because of a change in a plant running mode and environment, works such as changing control logic are required.
It is also possible to control a plant by using adaptive control or learning algorithm in order to automatically correct and change a control algorithm in accordance with a change in a plant running mode and environment. As one example of a plant control method using a learning algorithm, there have been proposed techniques about a control apparatus using reinforcement learning (e.g., refer to JP-A-2000-35956 (pp. 3 and 4, FIG. 3)). This control apparatus has a model for estimating the characteristics of control objects, and a learning unit for learning a method of generating a model input at which a model output can achieve a target value.
A boiler is a large structural body having a height as high as several ten meters, and it is difficult to grasp a phenomenon occurred in the inside thereof. A variety of phenomena occur in combination in the boiler, such as fuel combustion reaction, reaction of compositions of fuel gas, flow and thermal conduction phenomena of gas, water and vapor. Under these circumstances, various technologies have been developed in the field of hardware and control in order to suppress generation of hazardous substances in exhaust gas.
In the fields of boiler plant control, the main trend has been control logic on the basis of proportion/integration/differentiation (PID) control. In this case, it is difficult to grasp the distributions of steam temperature, steam pressure, gas composition densities and the like in the boiler used as control parameters, because the measurement areas of these control parameters are small as compared to the size of the boiler.
Providing many measurement points is difficult to be realized because of manufacture cost. Even if the number of measurement points is increased, the above-described composite phenomena are very difficult to be understood.
Technologies capable of flexibly processing the characteristics of a plant have been proposed by incorporating learning functions with teacher typically neural network, or learning functions with teacher by a reinforcement leaning method described in “Reinforcement Learning” translated by both Sadayoshi MIKAMI and Masaaki MINAKAWA, Morikita Shuppan K.K., Dec. 20, 2000. However, since it is difficult for all these technologies to confirm the cause and effect between an operation as a leaning result and a phenomenon, there is an issue of poor reliability of a new operation.
In the field of operator training, it is possible to learn a response of a boiler to an operation by using a training simulation apparatus. Techniques regarding this are disclosed in Japanese Patent Publication No. 3764585 (claim 1). According to Japanese Patent Publication No. 3764585 (claim 1), flame models of various burner combustion patterns generated by computer graphics are stored, and when cyclic images are to be reproduced, intermediate data is repetitively used to display burner combustion flame states and make a trainee understand the phenomena deeply.
If techniques described in JP-A-2000-35956 (pp. 3 and 4, FIG. 3) are utilized, a control algorithm can be automatically modified/changed in accordance with a change in a running mode and environment of a plant.
However, in order to evaluate the performance of learning results, it is necessary that a human being analyzes the learning results in detail. In order to run a plant safely, it is necessary to confirm whether a modified/changed algorithm runs correctly.
Namely, in order to use as plant control a learning algorithm capable of matching a change in a running mode and environment of a plant and running the plant easily, it is essential to improve reliability of the learning algorithm.